Bacterial diversity in water samples from uranium wastes as demonstrated by 16S rDNA and ribosomal intergenic spacer amplification retrievals
Bibliographic record
Abstract
Bacterial diversity was assessed in water samples collected from several uranium mining wastes in Ger many and in the United States by using 16S rDNA and ribosomal intergenic spacer amplification retrievals. The results obtained using the 16S rDNA retrieval showed that the samples collected from the uranium mill tailings of Schlema/Alberoda, Germany, were predominated by Nitrospina-like bacteria, whereas those from the mill tailings of Shiprock, New Mexico, USA, were predominated by gamma-Pseudomonas and Frauteria spp. Additional smaller populations of the Cytophaga-Flavobacterium-Bacteroides group and alpha- and delta-Proteobacteria were identified in the Shiprock samples as well. Proteobacteria and Cytophaga-Flavobacterium-Bacteroides were also found in the third uranium mill tailings studied, Gittersee/Coschütz, Germany, but the groups of the predominant clones were rather small. Most of the clones of the Gittersee/Coschütz samples represented individual sequences, which indicates a high level of bacterial diversity. The samples from the fourth uranium waste studied, Steinsee Deponie B1, Germany, were predominantly occupied by Acinetobacter spp. The ribosomal intergenic spacer amplification retrieval provided results complementary to those obtained by the 16S rDNA analyses. For instance, in the Shiprock samples, an additional predominant bacterial group was identified and affiliated with Nitrosomonas sp., whereas in the Gittersee/Coschütz samples, anammox populations were identified that were not retrieved by the applied 16S rDNA approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".